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Of the nearly 35 million people in the USA who are hearing impaired, only an estimated 25% use hearing aids (HA). A good number of HAs are prescribed but not used partially because of the time to convergence for best operation between the audiologist and user. To improve HA retention, it is suggested that a machine learning (ML) protocol could be established which improves initial HA configurations given a user's pure-tone audiogram. This study examines a ML clustering method to predict the best initial HA fitting from a corpus of over 90,000 audiogram-fitting pairs collected from hearing centers throughout the USA. We first examine the final HA comfort targets to determine a limited number of preset configurations using several multi-dimensional clustering methods (Birch, Ward, and kmeans). The goal is to reduce the amount of adjustments between the centroid, selected as a fitting configuration to represent the cluster, and the final HA configurations. This may be used to reduce the adjustment cycles for HAs or as preset starting configurations for personal sound amplification products (PSAPs). Using various classification methods, audiograms are mapped to a limited number of potential preset configurations. Finally, the average adjustment between the preset fitting targets and the final fitting targets is examined.

Although there exist nearly 35 × 10 6 hearing impaired people in the U.S., only an estimated 25% use hearing aids (HA), while others elect not to use prescribed HAs. Lack of HA acceptance can be attributed to several factors including (i) performance variability in diverse environments, (ii) time-to-convergence for best HA operating configuration, (iii) unrealistic expectations, and (iv) cost/insurance. This study examines a nationwide dataset of pure-tone audiograms and HA fitting configurations. An overview of data characteristics is presented, followed by use of machine learning clustering to suggest ways of obtaining effective starting configurations, thereby reducing time-to-convergence to improve HA retention.

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